Fault Estimation Research Articles

Fault-Tolerant Control for Quadcopters Under Actuator and Sensor Faults

Fault detection and diagnosis (FDD) methods and fault-tolerant control (FTC) have been the focus of intensive research across various fields to ensure safe operation, reduce costs, and optimize maintenance tasks. Unmanned aerial vehicles (UAVs), particularly quadcopters or quadrotors, are often prone to faults in sensors and actuators due to their complex dynamics and exposure to various external uncertainties. In this context, this work implements different FDD approaches based on the Kalman filter (KF) for fault estimation to achieve FTC of the quadcopter, considering different faults with nonlinear behaviors and the possibility of simultaneous occurrences in actuators and sensors. Three KF approaches are considered in the analysis: linear KF, extended KF (EKF), and unscented KF (UKF), along with three-stage and adaptive variations of the KF. FDD methods, especially the adaptive filter, could enhance fault estimation performance in the scenarios considered. This led to a significant improvement in the safety and reliability of the quadcopter through the FTC architecture, as the system, which previously became unstable in the presence of faults, could maintain stable operation when subjected to uncertainties.

Statistical and source characterization of the 2023 Kahramanmaraş Türkiye earthquake sequence

AbstractWe studied seismic features of the 2023 Kahramanmaraş Türkiye earthquake sequence by analyzing the spatiotemporal behavior of the b- and -p values and source characteristics of the mainshocks. We complemented our study by determining the regional stress field. The b-values in the region varied from 0.45 to 1.15. We observed a slight b-value decrease (Δb≈0.2) months before the two mainshocks. Our results exhibited complex b-value patterns on the fault planes and regular aftershock productivity rates (1.14 < p < 1.25). We compare static stress drop estimates derived from effective fault dimensions to those of finite-fault dimensions. Total effective stress drops (the sum of the stress drops of all fault segments) for the earthquake doublet were almost identical (~ 2.05 MPa), while those from finite-fault dimensions are somewhat lower (0.35 and 0.96 MPa). Based on a complete stress drop case, effective seismic efficiency was 0.65 and 0.43 for both mainshocks. The amount of partial stress drop was used to discriminate between different stress drop models. No clear model is discerned for the first mainshock, but a partial or even complete stress drop, assuming finite-fault dimensions, is supported by our measurements. Stress drop estimations derived from spectral analysis (25.46 and 34.39 MPa) agreed with global studies but larger than finite and effective fault estimates. Stress inversion results indicated that in the fault segments where the mainshocks occurred, the orientation of principal axes was consistent with a strike-slip regime. Conversely, the normal-faulting regime dominates adjacent areas of the main fault system.

Error governor for active fault tolerance in PID control of MIMO systems

Input saturation and actuator faults are common issues in control system engineering. This paper proposes an Error Governor (EG) policy that dynamically manages the feedback error to enhance the tracking error in Multiple Input-Multiple Output (MIMO) closed-loop system controlled by Proportional-Integral-Derivative (PID) regulators. By integrating an Adaptive Kalman Filter (AKF) for optimal fault estimation with the EG scheme, the solution enables fault-tolerant control without requiring modifications to the baseline controller, which can be impractical or unsuitable in certain applications. Furthermore, the proposed control scheme completely avoids windup, thereby replacing conventional Anti-Windup (AW) schemes for PIDs, and is computationally cheap and easy to implement, needing only the same inputs as conventional AW algorithms and the actuator fault estimation. Simulation results on the MIMO model of the Zagi flying-wing aerial vehicle show that the EG reduces the tracking error in presence of actuator faults by − 49.92 % in terms of Integral Absolute Error (IAE), outperforming conventional AW methods.

Model-predictive fault-tolerant control of safety-critical processes based on dynamic safe set

Industrial systems and chemical plants heavily rely on automation and control systems for seamless operations. However, the susceptibility of these systems to various faults poses threats to processes, leading to economic losses and safety risks. Here, a robust fault-tolerant control (FTC) strategy is developed that can take proactive measures during faults involving in-time activation of a backup controller, to ensure that the system remains within safe operational limits. It is based on the Dynamic Safe Set (DSS) which is the set of initial process states that meet safety constraints at all times, and the dynamic safety margin (DSM) which is the minimum distance from the DSS boundary. For just-in-time corrective action, a critical fault function is introduced, defined as the time required by the system to cross the DSS boundary under the nominal controller only. This critical fault function is calculated offline and is integrated with a real-time fault size estimation to formulate the controller reconfiguration logic to keep system within DSS. A linear functional observer is used to estimate fault size, combined with a predictive scheme, to enhance robustness during the transient period of fault estimation. This configuration avoids unnecessary control actions while ensuring timely intervention. The proposed FTC strategy is tested on an exothermic Continuous Stirred Tank Reactor (CSTR) case study. The results demonstrate the strategy's effectiveness in handling process faults, ensuring both stability and safety constraints are met. Thus, this paper contributes to the advancement of FTC ensuring the resilience of industrial systems in the face of unforeseen challenges.

Intelligent sliding mode fault-tolerant control for aircraft engines with actuator dynamics and faults based on adaptive dynamic programming

Abstract The fault-tolerant control issue of aircraft engines with actuator dynamics and faults is investigated in this paper. By proposing a novel intelligent sliding mode fault-tolerant control (ISMFTC) method, which combines an adaptive dynamic programming (ADP) sliding surface with Grey Wolf Optimizer (GWO) for controller parameter optimisation, the goal is to achieve quality steady-state and dynamic performance in aircraft engines while maintaining strong fault-tolerance properties. Firstly, by considering not only actuator dynamics but also actuator faults, an uncertain nonlinear cascaded model of aircraft engines is developed according to characteristic of aircraft engines and their actuators. Secondly, an ADP-based sliding surface is proposed for considered aircraft engine uncertain nonlinear cascaded system. It can obtain a certain sense of optimised performance, and could be solved by ADP strategy off-line as well. Thirdly, fault-tolerant controller is obtained on the basis of sliding mode theory and adaptive fault estimation law, namely, ADP-based ISMFTC controller. Meanwhile, GWO is integrated into the investigation of ADP-based ISMFTC controller, optimised designable control parameters are obtained subsequently. Besides, robustness analysis is elaborated according to Lyapunov theory, fault estimation error is bounded and states of closed-loop system are uniformly ultimately bounded. Simulation on a twin-shaft turbofan aircraft engine, indicates the effect of proposed ADP-based ISMFTC method.

Sliding Mode Fault-Tolerant Control for Nonlinear LPV Systems with Variable Time-Delay

This paper presents a robust sliding mode fault-tolerant control (FTC) strategy for a class of linear parameter variant (LPV) systems with variable time-delays and uncertainties. First fault estimation (FE) is conducted using a robust sliding mode observer, synthesized to simultaneously estimate the states and actuator faults of LPV polytopic delayed systems. Second, a sliding mode FTC is developed, ensuring all states of the closed-loop system converge to the origin. This paper presents an integrated sliding mode FTC strategy to achieve optimal robustness between the observer and controller models. The integrated design approach offers several advantages over traditional separated FTC methods. Our novel approach is based on incorporating adaptive law into the design of the Lyapunov–Krasovskii functional to improve both robustness and performance. This is achieved by combining the concept of sliding mode control (SMC) with the Lyapunov–Krasovskii function under the H∞ criteria, which plays a key role in guaranteeing the stability of this class of system. The effectiveness of the proposed method is demonstrated through a diesel engine example, which highlights the validity and benefits of the integrated and separated FTC strategy for uncertain nonlinear systems with time delays and the sliding mode control.

Adaptive fault estimation and accommodation for distributed parameter systems with coupled parabolic partial differential equations

This paper presents a novel model-based approach for fault detection, estimation and accommodation in linear distributed parameter systems (DPS) governed by parabolic partial differential equations (PDEs), prone to sensor and actuator faults. Unlike traditional methods, our approach utilises a filter-based observer directly employing the PDE representation and relies solely on boundary measurements, eliminating the need for extensive sensor arrays. By generating fault detection residuals from a comparison of measured and observer outputs, our method offers efficient fault detection. Innovative parameter update laws facilitate accurate estimation of fault parameters, enabling precise adjustment of the nominal controller to mitigate fault effects. Rigorous Lyapunov analysis ensures the robustness of our approach. Additionally, we derive a formula for estimating time-to-accommodation (TTA), providing valuable insights into fault resolution timelines. Simulations on a linearised diffusion process validate the effectiveness of our scheme, highlighting its superiority over existing approaches.

A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance.

Robust Fault Estimation and Fault‐Tolerant Control for a Class of Fuzzy Singularly Perturbed Systems With State Time Delay Based on Learning Observer

ABSTRACTThis article studies the problem of fault estimation (FE) and dynamic output fault‐tolerant control (DOFTC) for a class of Takagi–Sugeno (T–S) fuzzy singularly perturbed systems (SPSs) which subject to time delay, external disturbance and actuator fault. A new fault estimation and fault‐tolerant control scheme was proposed and designed for the influence of the change of perturbation parameter on the singularly perturbed system. This scheme adopts the Takagi–Sugeno (T–S) fuzzy model, learning observer and dynamic output feedback fault‐tolerant mechanism, so that the system has multi‐time‐scale dynamic stability. The results show that this method has more accurate estimation effect, faster convergence speed, and very fast steady‐state response of fault‐tolerant control when faults occur. Furthermore, when constructing the Lyapunov function, the improved matrix was selected to ensure that the closed‐loop system is stable for all , and at the same time, the conservativeness of the results was reduced. Finally, the feasibility and correctness of the proposed design method were illustrated through two numerical examples.

Interval estimation of sensor fault in rotary steerable drilling tools based on set-membership approach

The rotary steerable drilling tools (RSDTs) are invented for high-precision wellpath control. Fault diagnosis is essential for the RSDT as it can improve the reliability of the drilling processes. To estimate the sensor fault of the RSDT, this paper investigates the interval estimation problem for Lipschitz nonlinear systems with sensor fault via the set-membership approach. Firstly, the RSDT is modeled by a discrete-time Lipschitz nonlinear system with unknown inputs, noises, and parameter uncertainties. Next, based on the ellipsoid bundles, a set-membership fault estimator is presented, where the unknown inputs are decoupled. Additionally, a sufficient condition, which is derived based on the P-radius of the ellipsoid bundles, is put forward to design the parameters of the estimator with Lipschitz nonlinear terms. Then, the interval estimations of the states and fault are obtained via ellipsoid bundles. Finally, the efficiency of the proposed approach is evaluated through numerical simulations and experiments.

Coupled error super-twisted sliding mode active fault-tolerant control for robotic system with actuator fault.

This paper focuses on a robotic system with actuator fault and presents a sliding mode active fault-tolerant control method based on coupled position error. The actuator fault is first detected by a fault alerter with a predetermined threshold. After the fault is successfully detected, the fault is estimated by a fault observer. Coupling the position error with the weighted position error of different joints, a non-singular fast terminal sliding mode surface is constructed, and a super-twisted algorithm is introduced to design a coupled error super-twisted sliding mode controller (CESSMC). Furthermore, the fault estimation is incorporated with the CESSMC to accomplish the coupled error super-twisted sliding mode active fault-tolerant control. The stability and the finite-time convergence of the system are theoretically proved. Simulations and experiments are conducted to verify the effectiveness of the proposed method. The proposed method can achieve the position error convergence in finite time and reduce chattering in the control input. With the tight coupling between position error and weighted position error, the fault compensation effect and trajectory tracking accuracy can be improved.

Robust sliding mode observer based-simultaneous state and actuator fault estimation for a class of switching systems

Robust sliding mode observer based-simultaneous state and actuator fault estimation for a class of switching systems

Learning Observer Based Fault Estimation for a Class of Unmanned Marine Vehicles: The Switched System Approach

Learning Observer Based Fault Estimation for a Class of Unmanned Marine Vehicles: The Switched System Approach

Event-triggered distributed consensus control of heterogeneous multi-agent system under communication and actuator faults

In this paper, a heterogeneous leader-follower multi-agent system is studied under simultaneous time-varying communication faults and actuator faults. First, the state of the leader is modeled as the closed-loop reference model (CRM) where the states of the direct-connected followers are fed to the leader to improve the leader-follower tracking capability. An event-triggered communication mechanism is designed for the agent information sharing among its neighbors so as to reduce the communication burden. The designed event-triggered mechanism, capable of adjusting the triggering interval threshold within a certain range, can be applied in practical multi-robot collaborative control to accommodate the varying requirements for different triggering intervals. Considering the time-varying communication link failures, a new distributed event-triggered observer is designed for each follower to estimate the system states so as to reduce the state error, whereas the adaptive distributed event-triggered estimators are further designed for the non-directly connected followers to estimate the coefficient matrix of the leader system. Then, an estimator is designed for the actuator fault estimation to mitigate their impact on the system consistency. Finally, an adaptive control strategy is proposed to ensure the consistency of the leader-follower system under the time-varying communication link faults and actuator faults. It is also shown that Zeno behavior is excluded for each agent and the effectiveness of the proposed adaptive event-triggered control strategy is verified on a heterogeneous multi-agent system.

Event-triggered adaptive fault estimation and fault-tolerant control for strict-feedback nonlinear systems with full state constraints

Event-triggered adaptive fault estimation and fault-tolerant control for strict-feedback nonlinear systems with full state constraints

Distributed fault estimation of nonlinear dynamical systems over sensor networks: a non-fragile approach

PurposeThe purpose of this paper is to investigate the fault estimation issue of nonlinear dynamical systems via distributed sensor networks. Furthermore, based on the communication topology of sensor networks, the nonfragile design strategy considering the gain fluctuation is also adopted for distributed fault estimators.Design/methodology/approachBy means of intensive dynamical model transformation, sufficient conditions with disturbance attenuation performance are established to design desired fault estimator gains with the help of convex optimization.FindingsA novel distributed fault estimation framework for a class of nonlinear dynamical systems is established over a set of distributed sensor networks, where sampled data of sensor nodes via local information exchanges can be used for more efficiency.Originality/valueThe proposed distributed fault estimator gain fluctuations are taken into account for the nonfragile strategy, such that the distributed fault estimators are more applicable for practical sensor networks implementations. In addition, an illustrative example with simulation results are provided to validate the effectiveness and applicableness of the developed distributed fault estimation technique.

Simultaneous state and fault estimation for a class of nonlinear Markov jump systems by distributed fault-tolerant observers

This paper investigates the estimation problem of a class of nonlinear Markov jump systems with actuator and sensor faults. The main goal is to design a distributed fault-tolerant observer to estimate system states and actuator faults simultaneously. Firstly, a novel distributed observer network is constructed to compensate the missing information of unobservable nodes of the system. Next, a class of new redundant sensors are set up on each distributed observer node to obtain more output measurement samples. More importantly, when some of the mentioned sensors occur faults, an index of sensor heath level is constructed to characterize the quality of the faulty sensor information. Further, a novel algorithm is designed to mask low quality output information and filter out relatively healthy one automatically. Based on the selected healthy output information, the system state and actuator fault are estimated in the case of sensor failure. Finally, an example is provided to demonstrate the effectiveness of the proposed method.

Fixed-time active fault-tolerant control for dynamical systems with intermittent faults and unknown disturbances

In this article, the problem of fixed-time active fault-tolerant control is investigated for dynamical linear systems with intermittent faults and unknown disturbances. Unlike traditional active fault-tolerant control, fixed-time control is taken into account in this article since intermittent faults appear and disappear within a certain period of time. The entire active fault-tolerant control framework is composed of fault detection, fault isolation, fault and state estimation as well as the reconfigurable controller. Using the homogeneity-based observers, states and faults are well estimated and a fault diagnosis scheme is proposed for the sake of detecting and isolating intermittent faults in a fixed time. The fault-tolerant controller, which provides global practical fixed-time stability of the closed-loop system, has two switching states corresponding to the appearance and disappearance of intermittent faults. As a consequence, intermittent faults are compensated via the designed active fault-tolerant control method and the system reaches practical stability with the entire convergence time bounded in a fixed time. Finally, two examples are exploited to demonstrate the effectiveness of theoretical results.

Two-Stage Hyperelliptic Kalman Filter-Based Hybrid Fault Observer for Aeroengine Actuator under Multi-Source Uncertainty

An aeroengine faces multi-source uncertainty consisting of aeroengine epistemic uncertainty and the control system stochastic uncertainty during operation. This paper investigates actuator fault estimation under multi-source uncertainty to enhance the fault diagnosis capability of aero-engine control systems in complex environments. With the polynomial chaos expansion-based discrete stochastic model quantification, the optimal filter under multi-source uncertainty, the Hyperelliptic Kalman Filter, is proposed. Meanwhile, by treating actuator fault as unknown input, the Two-stage Hyperelliptic Kalman Filter (TSHeKF) is also proposed to achieve optimal fault estimation under multi-source uncertainty. However, considering that the biases of the model are often fixed for the individual, the TSHeKF-based fault estimation is robust and leads to inevitable conservativeness. By adding the additional estimation of the unknown deviation in state function caused by probabilistic system parameters, the hybrid fault observer (HFO) is proposed based on the TSHeKF and realizes conservativeness-reduced estimation for actuator fault under multi-source uncertainty. Numerical simulations show the effectiveness and optimality of the proposed HFO in state estimation, output prediction, and fault estimation for both single and multi-fault modes, when considering multi-source uncertainty. Furthermore, Monte Carlo experiments have demonstrated that the HFO-based optimal fault estimation is less conservative and more accurate than the Two-stage Kalman Filter and TSHeKF, providing better safety and more reliable aeroengine operation assurance.

Integrated Fault-Tolerant Control Design With Sampled-Output Measurements for Interval Type-2 Takagi-Sugeno Fuzzy Systems.

This article is concerned with the integrated design of fault estimation (FE) and fault-tolerant control (FTC) for uncertain nonlinear systems suffering from actuator faults and external disturbance. The uncertain nonlinear systems are characterized as the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, and IT2 membership functions are employed to effectively handle uncertainties. A fuzzy observer, utilizing only sampled-output measurements, is applied to simultaneously estimate actuator faults and system states. Based on the estimation, the fault-tolerant controller is designed to ensure the system stability under a predefined H∞ performance. The sampling behavior complicates the system dynamics and makes the integrated FTC design more challenging. To confront this issue, the discontinuous Lyapunov functional technique is exploited to enhance stability results by considering the sampling characteristic, upon which FE and FTC units are co-designed in the linear matrix inequality (LMI) framework. To further relax stability criteria, the analysis process incorporates the bound information of membership functions through the membership-function-dependent (MFD) method. Additionally, the relationship of mismatched premise variables resulting from the sampling scheme is also taken into account. Moreover, considering the imperfect premise matching (IPM) framework, the proposed fault-tolerant controller provides greater flexibility in selecting the shapes of membership functions and number of fuzzy rules that can vary from the counterpart of the fuzzy system. Finally, the efficacy of the proposed FTC technique is validated through a detailed numerical example.